Feature Extraction
sentence-transformers
Safetensors
Transformers
English
bert
sentence-similarity
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use avsolatorio/01-100-11-1-2-2-0-0-cls-normed-384-512_GIST_BAAI_bge-small-en-v1.5-20240208121054-best with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use avsolatorio/01-100-11-1-2-2-0-0-cls-normed-384-512_GIST_BAAI_bge-small-en-v1.5-20240208121054-best with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("avsolatorio/01-100-11-1-2-2-0-0-cls-normed-384-512_GIST_BAAI_bge-small-en-v1.5-20240208121054-best") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use avsolatorio/01-100-11-1-2-2-0-0-cls-normed-384-512_GIST_BAAI_bge-small-en-v1.5-20240208121054-best with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="avsolatorio/01-100-11-1-2-2-0-0-cls-normed-384-512_GIST_BAAI_bge-small-en-v1.5-20240208121054-best")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("avsolatorio/01-100-11-1-2-2-0-0-cls-normed-384-512_GIST_BAAI_bge-small-en-v1.5-20240208121054-best") model = AutoModel.from_pretrained("avsolatorio/01-100-11-1-2-2-0-0-cls-normed-384-512_GIST_BAAI_bge-small-en-v1.5-20240208121054-best") - Notebooks
- Google Colab
- Kaggle
{"loss": 0.3628, "learning_rate": 1.2591494527923427e-06, "epoch": 1.01, "step": 114500}
fe26638 verified - Xet hash:
- c07df9d9feb6e42635f1813a37fcd5838767e3f92adc844d0840bc0bcca87dfd
- Size of remote file:
- 133 MB
- SHA256:
- d4e875769d15fb2a2f9eb9ccbe76b9c6c1299ab7010e52764b2bed03c11930ff
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